A Robust Blockchain Readiness Index Model

TL;DR

This paper enhances the Blockchain Readiness Index by enabling it to handle missing data through new weighting schemes, improving its accuracy in ranking countries' blockchain adoption readiness.

Contribution

It introduces two novel weighting schemes, linear and sigmoid, to estimate missing indicator data in the Blockchain Readiness Index, improving its robustness.

Findings

The new weighting schemes improve classification accuracy.

The extended index effectively handles missing data.

The framework enhances country ranking reliability.

Abstract

As the blockchain ecosystem gets more mature many businesses, investors, and entrepreneurs are seeking opportunities on working with blockchain systems and cryptocurrencies. A critical challenge for these actors is to identify the most suitable environment to start or evolve their businesses. In general, the question is to identify which countries are offering the most suitable conditions to host their blockchain-based activities and implement their innovative projects. The Blockchain Readiness Index (BRI) provides a numerical metric (referred to as the blockchain readiness score) in measuring the maturity/readiness levels of a country in adopting blockchain and cryptocurrencies. In doing so, BRI leverages on techniques from information retrieval to algorithmically derive an index ranking for a set of countries. The index considers a range of indicators organized under five pillars: Government Regulation, Research, Technology, Industry, and User Engagement. In this paper, we further extent BRI with the capability of deriving the index - at the country level - even in the presence of missing information for the indicators. In doing so, we are proposing two weighting schemes namely, linear and sigmoid weighting for refining the initial estimates for the indicator values. A classification framework was employed to evaluate the effectiveness of the developed techniques which yielded to a significant classification accuracy.